VLD-45: A Big Dataset for Vehicle Logo Recognition and Detection
Shuo Yang, Chunjuan Bo, Junxing Zhang, Pengxiang Gao, Yujie Li, Seiichi Serikawa
Abstract
Vehicle logo detection (VLD) is a special and significant topic in object detection for vehicle identification system applications. Nevertheless, the range of the research and analysis for VLD are seriously narrow in the real complex scenes, although it’s a critical role in the object detection of small sizes. In this paper, we make further analysis work toward vehicle logo recognition and detection in real-world situations. To begin with, we propose a new multi-class VLD dataset, called VLD-45 (Vehicle Logo Dataset), which contains 45000 images and 50359 objects from 45 categories respectively. Our new dataset provides several research challenges involve in small sizes object, shape deformation, low contrast and so on. Meanwhile, we use 6 existing classifiers and 6 detectors to evaluate our dataset and show the baseline performance. According to the result, our dataset has very significant research value for the task of small-scale object detection. The dataset source: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YangShuoys/VLD-45-B-DATASET-Detection</uri>